Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set
In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is ad-dressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in terms of log BB. Prior to the model development, steps of data analysis that comprise chemical data curation, structural, cutoff and cluster analy-sis (CA) were conducted. Linear Discriminant Analysis (LDA) and Multiple Linear Regression (MLR) were used to fit clas-sification and correlation functions. The best LDA-based model showed overall accuracies over 85% and 83% and for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than 69% of the variance in the experimental logBBwas developed. A brief and general interpretation of proposed models allowed the estimation on how ‘near’ our computa-tional approach is to the factors that dete